Automatic object detection is helpful to find and identify target objects in an image. Humans may be able to recognize one or a few target objects in an image with little effort. However, it can be challenging for humans to find and identify a substantial number of target objects in an image. Target objects in images may look different from different viewpoints when displayed in different sizes and scales, or even in different rotated angles. Some computer-implemented methods may detect target objects based on their appearance or features. However, the accuracy of those object detection methods might be not good enough for some applications, such as cash crops or certain agricultural applications.
Object detection from aerial imagery becomes even more challenging when the number of potential target objects in the area of interest increases and the resolution of aerial images is limited. Relying on humans to find and identify target objects becomes infeasible when there are considerable amounts of potential target objects in large-scale areas. Increasing the resolution of aerial images may be helpful in increasing the accuracy of object detection. However, at the same time, performing object recognition and detection on a high-resolution image increases the computing complexity, which would constrain the feasibility and efficiency of certain applications.
Therefore, methods and systems are needed to quickly and precisely detect target objects from aerial imagery of an area of interest. The disclosed methods and systems are directed to overcoming or improving one or more of the problems set forth above and/or other problems in the prior art.